1. Field of the Invention
The present invention relates to a method of image segmentation, and more particularly, to an image segmentation method using a layered mesh structure.
2. Description of Related Art
Image segmentation is a technology extracting an object by classifying pixels that are elements of an image, and finding the boundary of the object in the image. For example, in order to find the shape and size of an object matter in an image, the image should be divided into two areas, an object and a background. The object plays an important role in interpretation and expression of an image. In particular, in an MPEG-4 visual standard, an image is coded in units of objects. This technology is referred as an object-based coding. When necessary, a variety of editing techniques for an object, such as combination, removal, and modification of an object, can be used so that an image can be reproduced or efficiently compressed.
For example, there can be seen frequently application examples using image segmentation, such as weather forecast providing weather services with a background of a virtual screen of a weather map, and an image field processed and shown by combining an object with a different background such as a virtual studio. The very core technology enabling these applications is an image segmentation technology extracting only a desired object and combining the object with a screen with a different background.
However, the image segmentation processing using a computer is a difficult engineering problem because there is no clear boundary between each image and the characteristic of each image is different. So far, a masking technology has been developed, and in the masking technology, a homogenous region having similar values is identified based on the characteristics of an image such as luminance, edge information, and geometrical information in an image, and regions having similar characteristics among the identified regions are combined. Then, by using the whole of combined regions, an image is masked.
As leading image segment algorithms developed so far, there are a threshold method, a region growing method, a split-and-merge method, a watershed method, and an edge-based method. Each method has its own characteristics and application fields.
First, the threshold method is the one that uses a threshold to divide an image into a part having values larger than the threshold and a part having values smaller than the threshold, to distinguish an object. However, there is a problem that it is difficult to practically apply the threshold method to a natural image, and the threshold method can be applied only to a binary image. Also, with the threshold method, it is impossible to extract only a specified region of interest in an image.
Next, the region growing method is the one that takes an arbitrary location in an image as a seed, and with this seed as a center, a region having similar characteristics such as luminance, edge, and color, is searched for. That is, when the characteristics of a small region being processed are the same as those of an adjacent small region, the regions are combined into one region and by expanding regions having the same characteristics little by little, image segmentation is performed ultimately for the entire image.
The split-and-merge method is the one that divides a region into small areas of a size to have similar characteristics, and if an area has characteristics similar to those of an adjacent area when compared, the two areas are combined into one, and this process is repeatedly performed.
However, the region growing method and the split-and-merge method described above require a huge amount of computation and repetition in order to search the entire image for regions having identical characteristics, and despite these efforts, there is a problem that it is difficult to extract an object with a desired precision.
Meanwhile, the watershed method is an improvement of the region growing method in which a process dividing an image into many regions and recombining simultaneously by using a plurality of seeds is repeatedly performed to gradually approach to a desired precision. However, for image segmentation with a desired precision, a huge amount of computation is required. Also, a fragmentation problem can occur at the boundary of an image. That is, the problem that part of an object is cut off or part of another object is overlapping can occur. Furthermore, in order to find the boundary of a continuous object, a huge amount of computation is required and in some cases, the method fails to find a continuous boundary.
As described above, while the image segmentation technologies developed so far require a huge amount of computation in order to extract an object, fragmentation occurs in the technologies such that an object cannot be extracted accurately, for example, part of an object is cut off or part of another object is incorrectly included, or it is unable to find a continuous boundary.